OpenRaiser/Intern-Atlas huggingface dataset:
ASYMP: Fault-tolerant Mining of Massive Graphs
Venue: arXiv cs.DC 2017
Year: 2017

We present ASYMP, a distributed graph processing system developed for the timely analysis of graphs with trillions of edges. ASYMP has several distinguishing features including a robust fault tolerance mechanism, a lockless architecture which scales seamlessly to thousands of machines, and efficient data access patterns to reduce per-machine overhead. ASYMP is used to analyze the largest graphs at Google, and the graphs we consider in our empirical evaluation here are, to the best of our knowledge, the largest considered in the literature. Our experimental results show that compared to previous graph processing frameworks at Google, ASYMP can scale to larger graphs, operate on more crowded clusters, and complete real-world graph mining analytic tasks faster. First, we evaluate the speed of ASYMP, where we show that across a diverse selection of graphs, it runs Connected Component 3-50x faster than state of the art implementations in MapReduce and Pregel. Then we demonstrate the scalability and parallelism of this framework: first by showing that the running time increases linearly by increasing the size of the graphs (without changing the number of machines), and then by showing the gains in running time while increasing the number of machines. Finally, we demonstrate the fault-tolerance properties for the framework, showing that inducing 50% of our machines to fail increases the running time by only 41%.
Chernoff Information between Gaussian Trees
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we aim to provide a systematic study of the relationship between Chernoff information and topological, as well as algebraic properties of the corresponding Gaussian tree graphs for the underlying graphical testing problems. We first show the relationship between Chernoff information and generalized eigenvalues of the associated covariance matrices. It is then proved that Chernoff information between two Gaussian trees sharing certain local subtree structures can be transformed into that of two smaller trees. Under our proposed grafting operations, bottleneck Gaussian trees, namely, Gaussian trees connected by one such operation, can thus be simplified into two 3-node Gaussian trees, whose topologies and edge weights are subject to the specifics of the operation. Thereafter, we provide a thorough study about how Chernoff information changes when small differences are accumulated into bigger ones via concatenated grafting operations. It is shown that the two Gaussian trees connected by more than one grafting operation may not have bigger Chernoff information than that of one grafting operation unless these grafting operations are separate and independent. At the end, we propose an optimal linear dimensional reduction method related to generalized eigenvalues.
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures
Venue: arXiv cs.LG 2017
Year: 2017

Objective: A clinical decision support tool that automatically interprets EEGs can reduce time to diagnosis and enhance real-time applications such as ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a specificity below 5% was the minimum requirement for clinical acceptance. We propose a highperformance classification system based on principles of big data and machine learning. Methods: A hybrid machine learning system that uses hidden Markov models (HMM) for sequential decoding and deep learning networks for postprocessing is proposed. These algorithms were trained and evaluated using the TUH EEG Corpus, which is the world's largest publicly available database of clinical EEG data. Results: Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. This system detects three events of clinical interest: (1) spike and/or sharp waves, (2) periodic lateralized epileptiform discharges, (3) generalized periodic epileptiform discharges. It also detects three events used to model background noise: (1) artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deep learning system can deliver a low false alarm rate on EEG event detection, making automated analysis a viable option for clinicians. Significance: The TUH EEG Corpus enables application of highly data consumptive machine learning algorithms to EEG analysis. Performance is approaching clinical acceptance for real-time applications.
Deep Architectures for Automated Seizure Detection in Scalp EEGs
Venue: arXiv cs.LG 2017
Year: 2017

Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign variants (e.g., slowing). Commercially available systems suffer from unacceptably high false alarm rates. Deep learning algorithms that employ high dimensional models have not previously been effective due to the lack of big data resources. In this paper, we use the TUH EEG Seizure Corpus to evaluate a variety of hybrid deep structures including Convolutional Neural Networks and Long Short-Term Memory Networks. We introduce a novel recurrent convolutional architecture that delivers 30% sensitivity at 7 false alarms per 24 hours. We have also evaluated our system on a held-out evaluation set based on the Duke University Seizure Corpus and demonstrate that performance trends are similar to the TUH EEG Seizure Corpus. This is a significant finding because the Duke corpus was collected with different instrumentation and at different hospitals. Our work shows that deep learning architectures that integrate spatial and temporal contexts are critical to achieving state of the art performance and will enable a new generation of clinically-acceptable technology.
Siamese LSTM based Fiber Structural Similarity Network (FS2Net) for Rotation Invariant Brain Tractography Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel deep learning architecture combining stacked Bi-directional LSTM and LSTMs with the Siamese network architecture for segmentation of brain fibers, obtained from tractography data, into anatomically meaningful clusters. The proposed network learns the structural difference between fibers of different classes, which enables it to classify fibers with high accuracy. Importantly, capturing such deep inter and intra class structural relationship also ensures that the segmentation is robust to relative rotation among test and training data, hence can be used with unregistered data. Our extensive experimentation over order of hundred-thousands of fibers show that the proposed model achieves state-of-the-art results, even in cases of large relative rotations between test and training data.
Rating Protocol Design for Extortion and Cooperation in the Crowdsourcing Contest Dilemma
Venue: arXiv cs.GT 2017
Year: 2017

Crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks. However, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Hence, incentive mechanisms are of great importance to overcome the inefficiency of the socially undesirable equilibrium. Existing incentive mechanisms are not effective in providing incentives for cooperation in crowdsourcing competitions due to the following features: heterogeneous workers compete against each other in a crowdsourcing platform with imperfect monitoring. In this paper, we take these features into consideration, and develop a novel game-theoretic design of rating protocols, which integrates binary rating labels with differential pricing to maximize the requester's utility, by extorting selfish workers and enforcing cooperation among them. By quantifying necessary and sufficient conditions for the sustainable social norm, we formulate the problem of maximizing the revenue of the requester among all sustainable rating protocols, provide design guidelines for optimal rating protocols, and design a low-complexity algorithm to select optimal design parameters which are related to differential punishments and pricing schemes. Simulation results demonstrate how intrinsic parameters impact on design parameters, as well as the performance gain of the proposed rating protocol.
The Merits of Sharing a Ride
Venue: arXiv cs.SI 2017
Year: 2017

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.
Inferring Formal Properties of Production Key-Value Stores
Venue: arXiv cs.DC 2017
Year: 2017

Production distributed systems are challenging to formally verify, in particular when they are based on distributed protocols that are not rigorously described or fully understood. In this paper, we derive models and properties for two core distributed protocols used in eventually consistent production key-value stores such as Riak and Cassandra. We propose a novel modeling called certified program models, where complete distributed systems are captured as programs written in traditional systems languages such as concurrent C. Specifically, we model the read-repair and hinted-handoff recovery protocols as concurrent C programs, test them for conformance with real systems, and then verify that they guarantee eventual consistency, modeling precisely the specification as well as the failure assumptions under which the results hold.
Disentangled Representations for Manipulation of Sentiment in Text
Venue: arXiv cs.CL 2017
Year: 2017

The ability to change arbitrary aspects of a text while leaving the core message intact could have a strong impact in fields like marketing and politics by enabling e.g. automatic optimization of message impact and personalized language adapted to the receiver's profile. In this paper we take a first step towards such a system by presenting an algorithm that can manipulate the sentiment of a text while preserving its semantics using disentangled representations. Validation is performed by examining trajectories in embedding space and analyzing transformed sentences for semantic preservation while expression of desired sentiment shift.
Aircraft trajectory control with feedback linearization for general nonlinear system
Venue: arXiv cs.SY 2017
Year: 2017

The feedback linearization method is further developed for the controller design on general nonlinear systems. Through the Lyapunov stability theory, the intractable nonlinear implicit algebraic control equations are effectively solved, and the asymptotically tracking performance is guaranteed. Moreover, it is proved that the controller may be used in an inverse-free version to the set-point control. With this method, a nonlinear aircraft outer-loop trajectory controller is developed. For the concern regarding the controller's robustness, the integral control technique is combined to counteract the adverse effect from modeling errors. Simulation results verify the well performance of the proposed controller.
Secure Transmission and Self-Energy Recycling for Wireless-Powered Relay Systems with Partial Eavesdropper Channel State Information
Venue: arXiv cs.IT 2017
Year: 2017

This paper focuses on the secure transmission of wireless-powered relay systems with imperfect eavesdropper channel state information (ECSI). For efficient energy transfer and information relaying, a novel two-phase protocol is proposed, in which the relay operates in full-duplex (FD) mode to achieve simultaneous wireless power and information transmission. Compared with those existing protocols, the proposed design possesses two main advantages: 1) it fully exploits the available hardware resource (antenna element) of relay and can offer higher secrecy rate; 2) it enables self-energy recycling (S-ER) at relay, in which the loopback interference (LI) generated by FD operation is harvested and reused for information relaying. To maximize the worst-case secrecy rate (WCSR) through jointly designing the source and relay beamformers coupled with the power allocation ratio, an optimization problem is formulated. This formulated problem is proved to be non-convex and the challenge to solve it is how to concurrently solve out the beamformers and the power allocation ratio. To cope with this difficulty, an alternative approach is proposed by converting the original problem into three subproblems. By solving these subproblems iteratively, the closed form solutions of robust beamformers and power allocation ratio for the original problem are achieved. Simulations are done and results reveal that the proposed S-ER based secure transmission scheme outperforms the traditional time-switching based relaying (TSR) scheme at a maximum WCSR gain of 80%.Results also demonstrate that the WCSR performance of the scheme reusing idle antennas for information reception is much better than that of schemes exploiting only one receive antenna.
Dynamic Load-Balancing Vertical Control for Large-Scale Software-Defined Internet of Things
Venue: arXiv cs.NI 2017
Year: 2017

As the global Internet of things increasingly is popular with consumers and business environment, network flow management has become an important topic to optimize the performance on Internet of Things. The rigid existing Internet of things (IoT) architecture blocks current traffic management technology to provide a real differentiated service for large-scale IoT. Software-defined Internet of Things (SD-IoT) is a new computing paradigm that separates control plane and data plane, and enables centralized logic control. In this paper, we first present a general framework for SD-IoT, which consists of two main components: SD-IoT controllers and SD-IoT switches. The controllers of SD-IoT uses resource pooling technology, and the pool is responsible for the centralized control of the entire network. The switches of SD-IoT integrate with the gateway functions, which is responsible for data access and forwarding. The SD-IoT controller pool is designed as a vertical control architecture, which includes the main control layer and the base control layer. The controller (main controller) of the main control layer interacts upward with the application layer, interacts with the base control layer downwards, and the controller (base controller) of the basic control layer interacts with the data forwarding layer. We propose a dynamic balancing algorithm of the main controller based on election mechanism and a dynamic load balancing algorithm of the basic controller based on the balanced delay, respectively. The experimental results show that the dynamic balancing algorithm based on the election mechanism can ensure the consistency of the messages between the main controllers, and the dynamic load balancing algorithm based on the balanced delay can balance between these different workloads in the basic controllers.
On quality of implementation of Fortran 2008 complex intrinsic functions on branch cuts
Venue: arXiv cs.MS 2017
Year: 2017

Branch cuts in complex functions in combination with signed zero and signed infinity have important uses in fracture mechanics, jet flow and aerofoil analysis. We present benchmarks for validating Fortran 2008 complex functions - LOG, SQRT, ASIN, ACOS, ATAN, ASINH, ACOSH and ATANH - on branch cuts with arguments of all 3 IEEE floating point binary formats: binary32, binary64 and binary128. Results are reported with 8 Fortran 2008 compilers: GCC, Flang, Cray, Oracle, PGI, Intel, NAG and IBM. Multiple test failures were revealed, e.g. wrong signs of results or unexpected overflow, underflow, or NaN. We conclude that the quality of implementation of these Fortran 2008 intrinsics in many compilers is not yet sufficient to remove the need for special code for branch cuts. The test results are complemented by conformal maps of the branch cuts and detailed derivations of the values of these functions on branch cuts, to be used as a reference. The benchmarks are freely available from cmplx.sf.net. This work will be of interest to engineers who use complex functions, as well as to compiler and maths library developers.
A Model for Data Citation in Astronomical Research using Digital Object Identifiers (DOIs)
Venue: arXiv cs.DL 2017
Year: 2017

Standardizing and incentivizing the use of digital object identifiers (DOIs) to aggregate and identify both data analyzed and data generated by a research project will advance the field of astronomy to match best practices in other research fields like geosciences and medicine. Increase in the use of DOIs will prepare the discipline for changing expectations among funding agencies and publishers, who increasingly expect accurate and thorough data citation to accompany scientific outputs. The use of DOIs ensures a robust, sustainable, and interoperable approach to data citation in which due credit is given to researchers and institutions who produce and maintain the primary data. We describe in this work the advantages of DOIs for data citation and best practices for integrating a DOI service in an astronomical archive. We report on a pilot project carried out in collaboration with AAS Journals. During the course of the 1.5 year pilot, over 75% of submitting authors opted to use the integrated DOI service to clearly identify data analyzed during their research project when prompted at the time of paper submission.
Paid Peering, Settlement-Free Peering, or Both?
Venue: arXiv cs.NI 2017
Year: 2017

With the rapid growth of congestion-sensitive and data-intensive applications, traditional settlement-free peering agreements with best-effort delivery often do not meet the QoS requirements of content providers (CPs). Meanwhile, Internet access providers (IAPs) feel that revenues from end-users are not sufficient to recoup the upgrade costs of network infrastructures. Consequently, some IAPs have begun to offer CPs a new type of peering agreement, called paid peering, under which they provide CPs with better data delivery quality for a fee. In this paper, we model a network platform where an IAP makes decisions on the peering types offered to CPs and the prices charged to CPs and end-users. We study the optimal peering schemes for the IAP, i.e., to offer CPs both the paid and settlement-free peering to choose from or only one of them, as the objective is profit or welfare maximization. Our results show that 1) the IAP should always offer the paid and settlement-free peering under the profit-optimal and welfare-optimal schemes, respectively, 2) whether to simultaneously offer the other peering type is largely driven by the type of data traffic, e.g., text or video, and 3) regulators might want to encourage the IAP to allocate more network capacity to the settlement-free peering for increasing user welfare.
Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues
Venue: arXiv cs.IT 2017
Year: 2017

This article presents advances in resource allocation (RA) for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing (UP) and power allocation (PA) algorithms. The former pairs the users to obtain the high capacity gain by exploiting the channel gain difference between the users, while the later allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divideand- next largest difference-based UP algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO-OMA is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on RA for downlink NOMA.
Why the Equifax Breach Should Not Have Mattered
Venue: arXiv cs.CR 2017
Year: 2017

Data security, which is concerned with the prevention of unauthorized access to computers, databases, and websites, helps protect digital privacy and ensure data integrity. It is extremely difficult, however, to make security watertight, and security breaches are not uncommon. The consequences of stolen credentials go well beyond the leakage of other types of information because they can further compromise other systems. This paper criticizes the practice of using clear-text identity attributes, such as Social Security or driver's license numbers -- which are in principle not even secret -- as acceptable authentication tokens or assertions of ownership, and proposes a simple protocol that straightforwardly applies public-key cryptography to make identity claims verifiable, even when they are issued remotely via the Internet. This protocol has the potential of elevating the business practices of credit providers, rental agencies, and other service companies that have hitherto exposed consumers to the risk of identity theft, to where identity theft becomes virtually impossible.
Limitation of SDMA in Ultra-Dense Small Cell Networks
Venue: arXiv cs.IT 2017
Year: 2017

Benefitting from multi-user gain brought by multi-antenna techniques, space division multiple access (SDMA) is capable of significantly enhancing spatial throughput (ST) in wireless networks. Nevertheless, we show in this letter that, even when SDMA is applied, ST would diminish to be zero in ultra-dense networks (UDN), where small cell base stations (BSs) are fully densified. More importantly, we compare the performance of SDMA, single-user beamforming (SU-BF) (one user is served in each cell) and full SDMA (the number of served users equals the number of equipped antennas). Surprisingly, it is shown that SU-BF achieves the highest ST and critical density, beyond which ST starts to degrade, in UDN. The results in this work could shed light on the fundamental limitation of SDMA in UDN.
PolicySpace: a modeling platform
Venue: arXiv cs.MA 2017
Year: 2017

Public Policy involves proposing changes to existing practices, alternatives, new habits. Citizens and institutions react accordingly, accepting, refuting or adapting. Agent-based modeling is a tool that can enrich the policy analysis package explicitly considering dynamics, space and individual-level interactions. This paper presents a modeling platform called PolicySpace that models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. We describe the basics of the model, its agents and markets, the tax scheme, the parametrization, and how to run the model. Finally, we validate the model and demonstrate an application of the fiscal analysis. Besides providing the basics of the platform, our results indicate the relevance of the rules of taxes transfer for cities' quality of life.